Experimental investigation of mechanical properties and multi-objective optimization of electronic, glass, and ceramic waste-mixed concrete.

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete.

Exploring the purity of chitin from crustacean sources using deep eutectic solvents: A machine learning approach.

Objective: Chitin a natural polymer is abundant in several sources such as shells of crustaceans, mollusks, insects, and fungi. Several possible attempts have been made to recover chitin because of its importance in biomedical applications in various forms such as hydrogel, nanoparticles, nanosheets, nanowires, etc. Among them, deep eutectic solvents have gained much consideration because of their eco-friendly and recyclable nature.

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness.

Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer's disorder detection using EEG signals.

Alzheimer's disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer's disease more crucial since it lessens the amount of work for staff members and improves diagnostic outcomes. The major aim of this work is "to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal".

Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

Introduction: Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images.

Sustainable developments in near-dry electrical discharge machining process using sunflower oil-mist dielectric fluid.

In this study, a near-dry electrical discharge machining (NDEDM) process has been conducted using compressed air mixed with a low quantity of biodegradable refined sunflower oil (called oil-mist) to investigate the machining characteristics. The Box-Behnken method looks at how oil flow rate (OR), air pressure (AR), spark current (SC), and pulse width (PW) affect gas emission concentration (GEC), material removal rate (MRR), and surface roughness (SR). The TOPSIS (The Technique for Order of Preference by Similarity to the Ideal Solution) technique estimates the parameter optimal set for the best machining characteristics.

Experimental investigation and multi-objective optimization of eco-friendly near-dry electrical discharge machining of shape memory alloy using Cu/SiC/Gr composite electrode.

The near-dry electrical discharge machining processes have been conducted using air-mist or gas mist as a dielectric fluid to minimize the environmental impacts. In this article, near-dry electrical discharge machining (NDEDM) experiments have been performed to improve machining performance using an oxygen-mist dielectric fluid, a copper composite electrode, and Cu-Al-Be polycrystalline shape memory alloy (SMA) work materials. The copper composite electrode is made up of 12 wt% silicon carbide and 9 wt% graphite particles.

Cryogenically treated and untreated stainless steel grade 317 in sustainable wire electrical discharge machining process: a comparative study.

In this research, the influences of cryogenically treated stainless steel grade 317 on the eco-friendly near-dry wire-cut electrical discharge machining (NDWEDM) processes have been investigated using the minimum quantity of water mixed with oxygen gas (oxygen mist) dielectric fluid. The stainless steel grade 317 has been applied to make the various biomedical and industrial components due to its high creep strength. The wire wear ratio (WWR) and cutting rate (CR) of NDWEDM are compared using cryogenically treated and untreated work materials by Taguchi's analysis.

Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT.

In recent trends, wireless sensor networks (WSNs) have become popular because of their cost, simple structure, reliability, and developments in the communication field. The Internet of Things (IoT) refers to the interconnection of everyday objects and sharing of information through the Internet. Congestion in networks leads to transmission delays and packet loss and causes wastage of time and energy on recovery.

An investigation on gas emission concentration and relative emission rate of the near-dry wire-cut electrical discharge machining process.

Wire-cut electrical discharge machining (WEDM) is the highly essential unconventional electrothermal machining process to cut the contour profile in the hard materials in modern production industries. The various environmental impacting contaminants (by evaporating and reacting liquid dielectric fluid) have been produced during the conventional WEDM process and are harmful to the machine operators. These wastes have been minimized by the near-dry WEDM process in which the pressurized air mixed with a small amount of water is used as a dielectric medium.

High temperature stable conjugated polyazomethines containing naphthalene moiety: Synthesis, characterization, optical, electrical and thermal properties.

A series of azomethine diol monomers containing naphthalene moiety were synthesized and oxidatively polymerized in aqueous alkaline medium using NaOCl as oxidant. The structure of the monomers and polymers was characterized by using various spectroscopic techniques. PL spectra of polymers are exhibiting good emission with high Stokes shift values than the monomers due to their polyconjugated structure.

An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine.

Skin is the outermost and largest organ of the human body that protects us from the external agents. Among the various types of diseases affecting the skin, melanoma (skin cancer) is the most dangerous and deadliest disease. Though it is one of the dangerous forms of cancer, it has a high survival rate if and only if it is diagnosed at the earliest.

Copper (0) Mediated Single Electron Transfer-Living Radical Polymerization of Methyl Methacrylate: Functionalized Graphene as a Convenient Tool for Radical Initiator.

Polymer nanocomposites have been synthesized by the covalent addition of bromide-functionalized graphene (Graphene-Br) through the single electron transfer-living radical polymerization technique (SET-LRP). Graphite functionalized with bromide for the first time via an efficient route using mild reagents has been designed to develop a graphene based radical initiator. The efficiency of sacrificial initiator (ethyl α-bromoisobutyrate) has also been compared with a graphene based initiator towards monitoring their Cu(0) mediated controlled molecular weight and morphological structures through mass spectroscopy (MOLDI-TOF) and field emission scanning electron microscopy (FE-SEM) analysis, respectively.

An Efficient Method for the Detection of Oblique Fissures from Computed Tomography images of Lungs.

Detection of a pulmonary fissure in lungs is difficult due to its anatomical changeability among humans and it is essential in the clinical environment for accurate localizing and treating the lung abnormalities on a lobe level in human lungs. In this work, an algorithmic approach is proposed to detect the lung oblique fissures from lung computed tomography (CT) images. In the preprocessing module of our approach, the lung structures are enhanced using morphological operation and lung images are de-noised using Wiener filter.

A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques.

A brain tumor is an accumulation of abnormal cells in human brain. As tumor increases in size, it induces brain damage. Hence it is essential to diagnose the type of brain tumor.

An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM.

The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy tissue but the malignant tumor can affect the neighboring tissues of brain that can lead to the death of person. An early detection of brain tumor can be required to protect the survival of patients.

Non-Linear Filtering Technique Used for Testing the Human Lumbar Spine FEA Model.

In this paper, the objective is to generate a mesh model of a spine that simulates numerically the biomedical properties of two vertebrae (L4 and L5) of human spine and an inter vertebrae disc using Finite Element Analysis (FEA) technique. Here, different types of non-linear filters and different edge detection techniques are used to segment the edges and the results are compared. The result shows that median filter obtains improved segmented output results in terms of edge length density, average magnitude, final threshold, initial position, and fine-tuned image.

A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer.

Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation.

An improved medical decision support system to identify the breast cancer using mammogram.

An improved Computer Aided Clinical Decision Support System has been developed to classify the tumor and identify the stages of the cancer using neural network and presented in this paper. The texture and shape features have been extracted and the optimal feature set has been obtained using multiobjective genetic algorithm (MOGA). The multilayer back propagation neural network with Ant Colony Optimization and Particle Swarm Optimization has been used.