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Angiogenic proteins (AGPs) play a critical role in both pathological and physiological activities, making them key therapeutic targets in diseases like cancer, heart disease, and stroke. Traditional methods for identifying AGPs are labor-intensive and time-consuming, creating a need for more efficient approaches. This study addresses this challenge by developing a novel computational model, Ens-Deep-AGP, designed to enhance AGP prediction. The model introduces innovative feature engineering techniques, including Position Specific Scoring Matrix-Decomposition-Discrete Cosine Transform (PSSM-DC-DCT) and Position Specific Scoring Matrix-Auto-Cross-Discrete Wavelet Transform (PSSM-ACC-DWT), which capture comprehensive protein sequence information. The ensemble feature set of these approaches are then fed into Multi-headed Ensemble Residual Convolutional Neural Network (MERCNN), a robust deep learning architecture. Ens-Deep-AGP achieved remarkable accuracy rates of 99.79 % on training dataset and 92.97 % on testing dataset, surpassing Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory Networks (BiLSTM). The successful prediction of AGPs is crucial for accelerating drug development, discovering novel therapeutic targets and deepen our understanding of AGPs' complex roles in healthcare., Competing Interests: Declaration of competing interest Authors have no competing interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose is measured by minimally invasive methods, which involve extracting a small blood sample and transmitting it to a blood glucose meter. This method is deemed discomforting for individuals who are undergoing it. The present study introduces an Explainable Artificial Intelligence (XAI) system, which aims to create an intelligible machine capable of explaining expected outcomes and decision models. To this end, we analyze abnormal glucose levels by utilizing Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). In this regard, the glucose levels are acquired through the glucose oxidase (GOD) strips placed over the human body. Later, the signal data is converted to the spectrogram images, classified as low glucose, average glucose, and abnormal glucose levels. The labeled spectrogram images are then used to train the individualized monitoring model. The proposed XAI model to track real-time glucose levels uses the XAI-driven architecture in its feature processing. The model's effectiveness is evaluated by analyzing the performance of the proposed model and several evolutionary metrics used in the confusion matrix. The data revealed in the study demonstrate that the proposed model effectively identifies individuals with elevated glucose levels., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Post-acute myocardial infarction (AMI) fibrosis is a pathophysiologic process characterised by activation of the profibrotic mediator, transforming growth factor-β (TGF-β). AMI is associated with a substantial increase in the levels of extracellular adenosine triphosphate (eATP), which acts on the purinergic P2X7-receptor (P2X7-R) and triggers an inflammatory response that contributes to myocardial fibrotic remodelling. P2X7-R has been implicated in several cardiovascular diseases; however, its role in the regulation of cardiac fibrosis remains unclear. Therefore, the current study aimed to determine the effect of the P2X7-R antagonist, A740003, on post-AMI fibrosis, via the profibrotic TGF-β1/Smad signalling pathway, and elucidate whether its effect is mediated via the modulation of GSK-3β. AMI was induced by surgical ligation of the left anterior descending coronary artery, Thereafter, animals were divided into groups: sham control, MI-untreated, MI-vehicle, and MI-A740003 (50 mg/kg/day) and treated for seven days accordingly. The heart weight/body weight ratio of untreated-ligated rats significantly increased by 15.1 %, creatine kinase-MB (CK-MB) significantly increased by 40 %, troponin-I levels significantly increased by 25.4 %, and lactate dehydrogenase significantly increased by 47.2 %, indicating myocardial damage confirmed by morphological changes and massive cardiac fibrosis. The protein expression of cardiac fibronectin, TGF-β1, and p-Smad2 were also upregulated by 143 %, 40 %, and 8 %, respectively, indicating cardiac fibrosis. The treatment of ligated rats with A740003 led to improvement in all the above-mentioned parameters. Overall, A740003 exhibits potential cardio-protective effects on post-AMI fibrotic remodelling in the animal model of AMI through P2X7-R blockade, possibly by downregulating the profibrotic TGF-β1/Smad signalling pathway and restoring GSK-3β phosphorylation. Altogether, treatment with A740003 could serve as a new cardioprotective strategy to attenuate post-AMI fibrotic remodelling., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions about it. With the advent of bi-directional deep learning algorithms and large-scale datasets, MRC achieved improved results. However, these models are still suffering from two research issues: textual ambiguities and semantic vagueness to comprehend the long passages and generate answers for abstractive MRC systems. To address these issues, this paper proposes a novel Extended Generative Pretrained Transformers-based Question Answering (ExtGPT-QA) model to generate precise and relevant answers to questions about a given context. The proposed architecture comprises two modified forms of encoder and decoder as compared to GPT. The encoder uses a positional encoder to assign a unique representation with each word in the sentence for reference to address the textual ambiguities. Subsequently, the decoder module involves a multi-head attention mechanism along with affine and aggregation layers to mitigate semantic vagueness with MRC systems. Additionally, we applied syntax and semantic feature engineering techniques to enhance the effectiveness of the proposed model. To validate the proposed model's effectiveness, a comprehensive empirical analysis is carried out using three benchmark datasets including SQuAD, Wiki-QA, and News-QA. The results of the proposed ExtGPT-QA outperformed state of art MRC techniques and obtained 93.25% and 90.52% F1-score and exact match, respectively. The results confirm the effectiveness of the ExtGPT-QA model to address textual ambiguities and semantic vagueness issues in MRC systems., Competing Interests: The authors declare there are no competing interests., (©2023 Ahmed et al.)

Recently, the number of drones has increased, and drones' illegal and malicious use has become prevalent. The dangerous and wasteful effects are substantial, and the probability of attacks is very high. Therefore, an anomaly detection and protection system are needed. This paper aims to design and implement an intelligent anomaly detection system for the security of unmanned aerial vehicles (UAVs)/drones. The proposed system is heavily based on utilizing ICMetric technology to exploit low-level device features for detection. This technology extracts the accelerometer and gyroscope sensors' bias to create a unique number known as the ICMetric number. Hence, ICMetric numbers represent additional features integrated into the dataset used to detect drones. This study performs the classification using a deep neural network (DNN). The experimental results prove that the proposed system achieves high levels of detection and performance metrics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ali Alheeti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Parvathaneni Naga Srinivasu et al.)

The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing online information, new opportunities emerged, and diverse issues have been raised, which have attracted researchers to address these research problems. In this current age, where online business and e-commerce are part of our daily lives, recommender systems (RSs) are very effective for information filtering. RSs play a significant role in our lives by assisting users in recommending items and services what they may be interesting in to purchase or avail. In this research work, our goal is to predict the users' ratings for various items, which are an active research area in collaborative filtering (CF). In this work, we have explored various similarity measures based on user-user and item-item rating predictions on different datasets by applying collaborative filtering approaches. The comparison of item-item and user-user CF algorithms such as user K-Nearest Neighbour using cosine; similarity, Pearson correlation as well as item-based K-NN using these measures with baseline approaches and matrix-based methods such as Matrix factorization (MF), biased MF, and factor wise MF has been carried out. For empirical-based comparison analysis, diverse approaches have been selected such as slope one, random, and global average, and it revealed that item-item K-NN using Pearson correlation has outperformed all other applied approaches. For the experiments, three real world and widely used datasets of MovieLens 1M, CiaoDVD, and MovieLens 100k have been used. The empirical-based results have been evaluated by using standard performance evaluation measures of RMSE and MAE., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Sofia Nudrat et al.)

The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there is a need to monitor more closely than ever the data being collected, shared, processed, and stored. The advent of cloud, IoT, and edge computing paradigms poses huge risks towards the privacy of data, especially, in the healthcare environment. However, there is a lack of comprehensive research focused on seeking efficient and effective solutions that ensure data privacy in the healthcare domain. The data being collected and processed by healthcare applications is sensitive, and its manipulation by malicious actors can have catastrophic repercussions. This paper discusses the current landscape of privacy-preservation solutions in IoT and edge healthcare applications. It describes the common techniques adopted by researchers to integrate privacy in their healthcare solutions. Furthermore, the paper discusses the limitations of these solutions in terms of their technical complexity, effectiveness, and sustainability. The paper closes with a summary and discussion of the challenges of safeguarding privacy in IoT and edge healthcare solutions which need to be resolved for future applications., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Naif Almusallam et al.)