A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Electrical studies of nanocomposites consisting of MWNTs and polystyrene

Multi-wall carbon nanotubes (MWNTs)/polystyrene (PS) nanocomposites containing different concentrations of MWNTs were prepared by the solution evaporation method. Dispersion of MWNTs in PS was achieved by using ultrasonic energy. The structure of nanocomposites was investigated using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The dielectric constant (ε′), dielectric loss (ε″), and ac conductivity (σ _ac) were measured from room temperature to 100 °C over the frequency range 10³ Hz–1.2 MHz. The results show that the dielectric properties depend on both frequency and temperature and they are enhanced by increasing the MWNTs content.     

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.     

Antioxidant and antithrombotic effects of green mussels (Perna canaliculus) in rats

In the past decade, the use of marine mussels as seafood is being more popular. They considered being a rich source of various nutritional bioactive compounds that aroused the scientific community's interest. This study investigated the antioxidant and the antithrombotic consequences on Sprague-Dawley male rats after adding dried New Zealand mussel Perna canaliculus in their diet. The biochemical, hematological and histopathological changes were also observed. Forty rats were divided into four groups according to the amount of dried mussels in their diet, in addition to a control group that consumed a basal diet only. Group 1 consumed 25% dried mussels in its basal diet; Group 2 consumed 35% dried mussels in its basal diet, and Group 3 was consumed 45% dried mussels in its basal diet. The biochemical parameters showed improvements in liver function. Interestingly, the lipid profile decreased, especially the low-density lipoprotein cholesterol (LDL-C) levels which were reduced significantly in Group 3 (p < .01). These observations were accompanied by a decrease in iron levels significantly as the amount of dried mussels increased (p < .01). Furthermore, the noticed changes in the hematological profile prove that there is an increase in antithrombotic activity. Dried mussels had potent antioxidant effects in the liver as shown by increased lipid peroxide (p < .05), reduced glutathione (p < .05), and glutathione peroxidase (GSH-Px; p < .05). Additionally, antioxidant activity in the kidney was shown to increase through GSH-Px activity (p < .01). In conclusion, these results indicate that consuming dried mussels resulted in improved biochemical and antioxidants activities and could be used as an antithrombotic agent.     

Efficient homogeneously weighted dispersion control charts with an application to distillation process

Monitoring disturbances in process dispersion using control chart is mostly based on the assumption that the quality characteristic follows normal distribution, which is not the case in many real-life situations. This paper proposes a set of new dispersion charts based on the homogeneously weighted moving average (HWMA) scheme, for efficient detection of shifts in process standard deviation (σ). These charts are based on a variety of σ estimators and are investigated for normal as well as heavy tailed symmetric and skewed distributions. The shift detection ability of the charts is evaluated using different run length characteristics, such as average run length (ARL), extra quadratic loss (EQL), and relative ARL measures. The performance of the proposed HWMA control charts is also compared with the existing EWMA dispersion charts, using different design parameters. Furthermore, an illustrative example is presented to monitor the vapor pressure in a distillation process.     

Cache Memory Design for Single Bit Architecture with Different SenseAmplifiers

Most modern microprocessors have one or two levels of on-chip caches to make things run faster, but this is not always the case. Most of the time, these caches are made of static random access memory cells. They take up a lot of space on the chip and use a lot of electricity. A lot of the time, low power is more important than several aspects. This is true for phones and tablets. Cache memory design for single bit architecture consists of six transistors static random access memory cell, a circuit of write driver, and sense amplifiers (such as voltage differential sense amplifier, current differential sense amplifier, charge transfer differential sense amplifier, voltage latch sense amplifier, and current latch sense amplifier, all of which are compared on different resistance values in terms of a number of transistors, delay in sensing and consumption of power. The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34 μW of power which shows that power is reduced up to 83%, 77.75% reduction in the case of the current differential sense amplifier, 39.62% in case of charge transfer differential sense amplifier and 50% in case of voltage latch sense amplifier when compared to existing latch sense amplifier architecture. Furthermore, power reduction techniques are applied over different blocks of cache memory architecture to optimize energy. The single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078 μW of power, i.e., reduce 28% more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.     

TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.     

Cold Sprayability of Mixed Commercial Purity Ti Plus Ti6Al4V Metal Powders

In the present work, metallic composite coatings of commercial purity Ti plus Ti6Al4V were produced by cold spraying to explore the effect of mixing on porosity and mechanical properties of the coatings. The coatings were deposited using N₂ gas at 800 °C and 4 MPa pressure on 1020 steel substrate. Coating characteristics were studied by examining porosity percentages and Vickers’s hardness. The microstructure was examined using optical and electron microscopy techniques. It was observed that mixing metal powders can lead to improvements in cold sprayability, specifically decreases in the porosity of the ‘matrix’ powder. It is shown that a critical addition can significantly influence porosity, but above this critical level, there is a little change in porosity. Hardness differences between the two powders are considered to be the first-order influence, but differences in particle sizes and morphology may also be contributing factors.