Enhancing heart disease prediction using a self-attention-based transformer model.

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning.

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable's impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model's multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.

Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System.

Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN-GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.

Second-life battery systems for affordable energy access in Kenyan primary schools.

As the world transitions to net zero, energy storage is becoming increasingly important for applications such as electric vehicles, mini-grids, and utility-scale grid stability. The growing demand for storage will constrain raw battery materials, reduce the availability of new batteries, and increase the rate of battery retirement. As retired batteries are difficult to recycle into components, to avoid huge amounts of battery waste, reuse and repurposing options are needed. In this research, we explore the feasibility of using second-life batteries (which have been retired from their first intended life) and solar photovoltaics to provide affordable energy access to primary schools in Kenya. Based on interviews with 12 East African schools, realistic system sizes were determined with varying solar photovoltaic sizes (5-10 kW in 2.5 kW increments) and lithium-ion battery capacities (5-20 kWh in 5 kWh increments). Each combination was simulated under four scenarios as a sensitivity analysis of battery transportation costs (i.e., whether they are sourced locally or imported). A techno-economic analysis is undertaken to compare new and second-life batteries in the resulting 48 system scenarios in terms of cost and performance. We find that second-life batteries decrease the levelized cost of electricity by 5.6-35.3% in 97.2% of scenarios compared to similar systems with new batteries, and by 41.9-64.5% compared to the cost of the same energy service provided by the utility grid. The systems with the smallest levelized cost of electricity (i.e., 0.11 USD/kWh) use either 7.5 kW or 10 kW of solar with 20 kWh of storage. Across all cases, the payback period is decreased by 8.2-42.9% using second-life batteries compared to new batteries; the system with the smallest payback period (i.e., 2.9 years) uses 5 kW solar and 5 kWh storage. These results show second-life batteries to be viable and cost-competitive compared to new batteries for school electrification in Kenya, providing the same benefits while reducing waste.

Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems.

The hybrid combination between underwater optical wireless communication (UOWC) and radio frequency (RF) is a vital demand for enabling communication through the air-water boundary. On the other hand, non-orthogonal multiple access (NOMA) is a key technology for enhancing system performance in terms of spectral efficiency. In this paper, we propose a downlink NOMA-based dual-hop hybrid RF-UOWC with decode and forward (DF) relaying. The UOWC channels are characterized by exponential-generalized Gamma (EGG) fading, while the RF channel is characterized by Rayleigh fading. Exact closed-form expressions of outage probabilities and approximated closed-form expressions of ergodic capacities are derived, for each NOMA individual user and the overall system as well, under the practical assumption of imperfect successive interference cancellation (SIC). These expressions are then verified via Monte-Carlo simulation for various underwater scenarios. To gain more insight into the system performance, we analyzed the asymptotic outage probabilities and the diversity order. Moreover, we formulated and solved a power allocation optimization problem to obtain an outage-optimal performance. For the sake of comparison and to highlight the achievable gain, the system performance is compared against a benchmark orthogonal multiple access (OMA)-based system.

Assessment of community pharmacy management towards self-medication requests of tetracyclines for pregnant women: a simulated client study in Upper Egypt.

Background Self-medication is a worldwide phenomenon of using medications without medical supervision. It is even more prevalent in low-income countries, where individuals seek community pharmacies because of accessibility and affordability. Although self-medication is associated with an increased risk of medication errors, few studies have been conducted to examine the quality of community pharmacy management towards self-medicating individuals of at-risk populations such as pregnant women. Objective We sought to investigate the quality of community pharmacies management of self-medication requests of tetracyclines for pregnant women. Setting The study was conducted in community pharmacies in Minya, Egypt. Methods A random sample of 150 community pharmacies was chosen from the urban areas of five districts of Minya, Egypt. To evaluate the actual practice, a simulated client was trained to visit pharmacies and purchase doxycycline for a pregnant woman. In a random subset of the sampled pharmacies (n = 100), interviews were conducted to evaluate pharmacy staff knowledge and attitudes regarding information gathering and dispensing practice. Main outcome measure Dispensing rate of doxycycline for pregnant women. Results From simulated client visits, almost all pharmacy staff (99.1%) dispensed doxycycline without requesting a prescription or collecting any information. About 25% of staff members did not abstain from dispensing even after knowing about pregnancy. On the other hand, most interviewed pharmacy staff (91.5%) reported that they ask about pregnancy before dispensing. Conclusion Our findings show that the current community pharmacy practice puts pregnant women at high risk of experiencing harmful self-medication outcomes. Therefore, strict legislative measures and pharmacy education programs should be considered in Egypt to lessen inappropriate dispensing rates in community pharmacies.

Optimization of Time Synchronization and Algorithms with TDOA Based Indoor Positioning Technique for Internet of Things.

To provide high-precision positioning for Internet of Things (IoT) scenarios, we optimize the indoor positioning technique based on Ultra-Wideband (UWB) Time Difference of Arrival (TDOA) equipment. This paper analyzes sources of positioning error and improves the time synchronization algorithm based on the synchronization packet. Then we use the labels of the known position to further optimize the time synchronization performance, and hence improve TDOA measurements. After time synchronization optimization, a Weighted Least Square (WLS) and Taylor coordination algorithm is derived. Experiments show that our optimization reduces the average positioning error from 54.8 cm to 12.6 cm.

Performance Analysis of Cooperative and Non-Cooperative Relaying over VLC Channels.

The line-of-sight (LoS) channel is one of the requirements for efficient data transmission in visible-light communications (VLC), but this cannot always be guaranteed in indoor applications for a variety of reasons, such as moving objects and the layout of rooms. The relay-assisted VLC system is one of the techniques that can be used to address this issue and ensures seamless connectivity. This paper investigates the performance of half-duplex (HD) conventional DF relay system and cooperative systems (i.e., selective DF (SDF) and incremental DF (IDF)) over VLC channels in terms of outage probability and energy consumption. Analytical expressions for both outage probability and the minimum energy-per-bit performance of the aforementioned relaying systems are derived. Furthermore, Monte Carlo simulations are provided throughout the paper to validate the derived expressions. The results show that exploiting SDF and IDF relaying schemes can achieve approximately 25% and 15% outage probability enhancement compared to single-hop and DF protocols, respectively. The results also demonstrate that the performance of the single-hop VLC system deteriorates when the end-to-end distances become larger. For example, when the vertical distance is 3.5m, the single-hop approach consumes 20%, 40% and 45% more energy in comparison to the DF, SDF, and IDF approaches, respectively.

Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks.

Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.

Quality of Community Pharmacy Practice in Antibiotic Self-Medication Encounters: A Simulated Patient Study in Upper Egypt.

Antibiotic misuse, either by patients or healthcare professionals, is one of the major contributing factors to antimicrobial resistance. In many Middle Eastern countries including Egypt, there are no strict regulations regarding antibiotic dispensing by community pharmacies. In this study, we examined antibiotic dispensing patterns in Egyptian community pharmacies. About 150 community pharmacies were randomly chosen using convenience sampling from the five most populous urban districts of Minia Governorate in Egypt. Two simulated patient (SP) scenarios of viral respiratory tract infection requiring no antibiotic treatment were used to assess the actual antibiotics dispensing practice of. Face-to-face interviews were then conducted to assess the intended dispensing practice. Descriptive statistics were calculated to report the main study outcomes. In 238 visits of both scenarios, 98.3% of service providers dispensed amoxicillin. Although stated otherwise in interviews, most pharmacy providers (63%) dispensed amoxicillin without collecting relevant information from presenting SPs. Findings showed high rates of antibiotic misuse in community pharmacies. Discrepancies between interviews and patient simulation results also suggest a practice‒knowledge gap. Corrective actions, whether legislation, enforcement, education, or awareness campaigns about antibiotic misuse, are urgently needed to improve antibiotic dispensing practices in Egyptian community pharmacies.