Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Introduction: Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. Methods: This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Results: Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. Discussion: We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

LMBiS-Net: A lightweight bidirectional skip connection based multipath CNN for retinal blood vessel segmentation.

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net's robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications.

Advanced machine learning approach for DoS attack resilience in internet of vehicles security.

Recent years have witnessed security as a great concern in vehicular networks (VANET). Particularly, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can jeopardize the network by broadcasting a storm of packets. Correspondingly, the network resources are jammed with malicious traffic. In this connection, the existing research presented various techniques to cope with DoS and DDoS attacks. Different from those traditional approaches, this study proposes an Intelligent Intrusion Detection System (IDS) by leveraging Machine Learning (ML). The proposed IDS utilizes a publicly available dataset on the application layer for mitigating DDoS attacks. The designed ML-based IDS relies on combining both the Random Projection (RP) and Randomized Matrix Factorization (RMF) methods to achieve the best results for enhancing the detection capabilities of the IDS. This amalgamation enhances the system's detection capabilities by extracting and analyzing meaningful features from network traffic data. Experimental validation of our approach involves a comprehensive evaluation of various ML models, including Extra Tree Classifier (ETC), Logistic Regression (LR), and Random Forest (RF). Remarkably, the combined accuracy of these models yields an average system accuracy of 0.98, surpassing existing methods. Unlike conventional approaches, our proposed IDS excels in efficiency and exhibits notable performance in detecting DoS and DDoS attacks in VANET. This proficiency ensures the integrity and safety of vehicle communications. Thus, our research substantially contributes to the vehicular network security field. The presented findings establish a foundation for future advancements in securing connected vehicles.

Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix.

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues.

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.

AD-CAM: Enhancing Interpretability of Convolutional Neural Networks with a Lightweight Framework - From Black Box to Glass Box.

In the realm of machine vision, the convolutional neural network (CNN) is a frequently used and significant deep learning method. It is challenging to comprehend how predictions are formed since the inner workings of CNNs are sometimes seen as a black box. As a result, there has been an increase in interest among AI experts in creating AI systems that are easier to understand. Many strategies have shown promise in improving the interpretability of CNNs, including Class Activation Map (CAM), Grad-CAM, LIME, and other CAM-based approaches. These methods do, however, have certain drawbacks, such as architectural constraints or the requirement for gradient computations. We provide a simple framework termed Adaptive Learning based CAM (Adaptive-CAM) to take advantage of the connection between activation maps and network predictions. This framework includes temporarily masking particular feature maps. According to the Average Drop-Coherence-Complexity (ADCC) metrics, our method outperformed Score-CAM and another CAM-based activation map strategy in Residual Network-based models. With the exception of the VGG16 model, which witnessed a 1.94% decline in performance, the performance improvement spans from 3.78% to 7.72%. Additionally, Adaptive-CAM generates saliency maps that are on par with CAM-based methods and around 153 times superior to other CAM-based methods.

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.

Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.

Internet of Things-based sustainable environment management for large indoor facilities.

Due to global warming and climate change, the poultry industry is heavily impacted, especially the broiler industry, due to the sensitive immune system of broiler chickens. However, the continuous monitoring and controlling of the farm's environmental parameters can help to curtail the negative impacts of the environment on chickens' health, leading to increased meat production. This article presents smart solutions to such issues, which are practically implemented, and have low production and operational costs. In this article, an Internet of Things (IoT) based environmental parameters monitoring has been demonstrated for the poultry farmhouse. This system enables the collection and visualization of crucially sensed data automatically and reliably, and at a low cost to efficiently manage and operate a poultry farm. The proposed IoT-based remote monitoring system collects and visualizes environmental parameters, such as air temperature, relative humidity (RH), oxygen level (O2), carbon dioxide (CO2), carbon monoxide (CO), and ammonia (NH3) gas concentrations. The wireless sensor nodes have been designed and deployed for efficient data collection of the essential environmental parameters that are key for monitoring and decision-making process. The hardware is implemented and deployed successfully at a site within the control shed of the poultry farmhouse. The results revealed important findings related to the environmental conditions within the poultry farm. The temperature inside the control sheds remained within the desired range throughout the monitoring period, with daily average values ranging from 32 °C to 34 °C. The RH showed slight variations monitoring period, ranging from 65% to 75%, with a daily average of 70%. The O2 concentration exhibited an average value of 17% to 18.5% throughout the monitoring period. The CO2 levels showed occasional increases, reaching a maximum value of 1,100 ppm. However, this value was below the maximum permissible level of 2,500 ppm, indicating that the ventilation system was effective in maintaining acceptable CO2 levels within the control sheds. The NH3 gas concentration remained consistently low throughout the duration, with an average value of 50 parts per million (ppm).

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution.

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.

Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.

Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features.

A recently discovered coronavirus (COVID-19) poses a major danger to human life and health across the planet. The most important step in managing and combating COVID-19 is to accurately screen and diagnose affected people. The imaging technology of lung X-ray is a useful imaging identification/detection approach among them. The help of such computer-aided machines and diagnoses to examine lung X-ray images of COVID-19 instances can give supplemental assessment ideas to specialists, easing their workload to some level. The novel concept of this study is a hybridized approach merging pertinent manual features with deep spatial features for the classification of COVID-19. Further, we employed traditional transfer learning techniques in this investigation, utilizing four different pre-trained CNN-based deep learning models, with the Inception model showing a reasonably accurate result and a diagnosis accuracy of 82.17%. We provide a successful diagnostic approach that blends deep characteristics with machine learning classification to further increase clinical performance. It employs a complete diagnostic model. Two datasets were used to test the suggested approach, and it did quite well on several of them. On 1102 lung X-ray scans, the model was originally evaluated. The results of the experiments indicate that the suggested SVM model has a diagnostic accuracy of 95.57%. When compared to the Xception model's baseline, the diagnostic accuracy had risen by 17.58 percent. The sensitivity, specificity, and AUC of the proposed models were 95.37 percent, 95.39%, and 95.77%, respectively. To show the adaptability of our approach, we also verified our proposed model on other datasets. Finally, we arrived at results that were conclusive. When compared to research of a comparable kind, our suggested CNN model has a greater accuracy of classification and diagnostic effectiveness.

A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images.

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.

NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs' fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R2). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R2 of 0.929.

Joint Placement and Device Association of UAV Base Stations in IoT Networks.

Drone base stations (DBSs) have received significant research interest in recent years. They provide a flexible and cost-effective solution to improve the coverage, connectivity, quality of service (QoS), and energy efficiency of large-area Internet of Things (IoT) networks. However, as DBSs are costly and power-limited devices, they require an efficient scheme for their deployment in practical networks. This work proposes a realistic mathematical model for the joint optimization problem of DBS placement and IoT users' assignment in a massive IoT network scenario. The optimization goal is to maximize the connectivity of IoT users by utilizing the minimum number of DBS, while satisfying practical network constraints. Such an optimization problem is NP-hard, and the optimal solution has a complexity exponential to the number of DBSs and IoT users in the network. Furthermore, this work also proposes a linearization scheme and a low-complexity heuristic to solve the problem in polynomial time. The simulations are performed for a number of network scenarios, and demonstrate that the proposed heuristic is numerically accurate and performs close to the optimal solution.

Towards Void Hole Alleviation by Exploiting the Energy Efficient Path and by Providing the Interference-Free Proactive Routing Protocols in IoT Enabled Underwater WSNs.

Nowadays, the Internet of Things enabled Underwater Wireless Sensor Network (IoT-UWSN) is suffering from serious performance restrictions, i.e., high End to End (E2E) delay, low energy efficiency, low data reliability, etc. The necessity of efficient, reliable, collision and interference-free communication has become a challenging task for the researchers. However, the minimum Energy Consumption (EC) and low E2E delay increase the performance of the IoT-UWSN. Therefore, in the current work, two proactive routing protocols are presented, namely: Bellman⁻Ford Shortest Path-based Routing (BF-SPR-Three) and Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three). Then we formalized the aforementioned problems to accomplish the reliable data transmission in Underwater Wireless Sensor Network (UWSN). The main objectives of this paper include minimum EC, interference-free transmission, void hole avoidance and high Packet Delivery Ratio (PDR). Furthermore, the algorithms for the proposed routing protocols are presented. Feasible regions using linear programming are also computed for optimal EC and to enhance the network lifespan. Comparative analysis is also performed with state-of-the-art proactive routing protocols. In the end, extensive simulations have been performed to authenticate the performance of the proposed routing protocols. Results and discussion disclose that the proposed routing protocols outperformed the counterparts significantly.

An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification.

BACKGROUND: Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. METHODS: In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. RESULTS: The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. CONCLUSION: The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.