Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images.

In the medical field, Artificial Intelligence (AI) for diagnostic processes, particularly through deep learning techniques, has become increasingly advanced. Minor trauma, such as accidental cheek biting, sharp dental edges, or poorly fitting dentures, typically causes painful mouth ulcers and bump-like sores inside the mouth. Traditionally, diagnosing these ulcers involves a dentist or physician performing a physical examination, visually assessing the sores, and asking detailed questions about their size, location, duration, and related symptoms.

Investigation on the Photodegradation Performance of S-Rich g-CN/MnO Composites for Efficient Removal of Hazardous Dyes From Aqueous Phase.

Sulfur (S)-rich g-CN/α-MnO (α-MnO/GCN-S) heterojunction was synthesized using a simple hydrothermal technique, which was effectively utilized for the photodegradation of methylene blue and congo red dyes under visible light illumination. The results of the characterization analysis suggest that the α-MnO/GCN-S composite exhibits a high crystalline nature and the heterojunction successfully formed between g-CN and α-MnO demonstrates an effective photocatalytic agent for selected hazardous dyes. According to the obtained findings, α-MnO/GCN-S composite exhibited outstanding visible light absorption when compared with pure g-CN and α-MnO under visible light irradiation.

Unveiling optimal mother wavelets by COPRAS Method Analyzing speech signals despite face mask and shield obstacles.

Wavelet analysis is a prominent time-frequency analysis method in investigating various signals such as speech, vibration, acoustic signals, ultrasound, and underwater acoustic signals. Throughout the coronavirus pandemic, people have adopted diverse face shields and face masks, which have caused difficulties in understanding speech. To address this issue, the wavelet transform (WT), a proven effective method, can be implemented.

Fabrication of spinel NiCoO nanoflowers by simple hydrothermal method for effective electrochemical detection of NO in processed food sample.

This study introduces a novel, cost-effective, highly sensitive electrochemical sensor for detecting nitrite (NO) in processed food samples. The sensor was developed by fabricating spinel NiCoO nanoflowers (NCO) using a hydrothermal method. Various characterization techniques, including XRD, FT-IR, XPS, HR-SEM, EDX, and HR-TEM, were used to analyze the structure and morphology of NCO.

Synthesis of zirconium-based metal-organic framework/gelatin aerogel for removing phosphate and fluoride from aqueous solutions.

This study describes the preparation of novel hybrid aerogels derived from gelatin (Gel), incorporating Br-functionalized zirconium-based metal-organic framework (UiO-66-Br; MOF) as modifying agent to effectively eliminate phosphate and fluoride ions from aqueous environments. The adsorption performance of MOF decorated Gel (Gel-xMOF) hybrid aerogels was investigated under different conditions, including agitation time, adsorbent dosage, solution pH, initial phosphate and fluoride concentrations, coexisting ions, and temperature. The functional groups of the gelatin network, coupled with UiO-66-Br, enhanced the adsorption performance of phosphate and fluoride ions from aqueous solutions.

Classification of motor imagery EEG with ensemble RNCA model.

Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements.

Synthesis of silver‑bismuth oxide encapsulated hydrazone functionalized chitosan (AgBiO/FCS) nanocomposite for electrochemical sensing of glucose, HO and Escherichia coli O157:H7.

In this work, silver‑bismuth oxide encapsulated 1,3,5-triazine-bis(4-methylbenzenesulfonyl)-hydrazone functionalized chitosan (SBO/FCS) nanocomposite was synthesized by a simple hydrothermal method. The amine (-NH) group was functionalized by the addition of cyanuric acid chloride followed by 4-methylbenzenesulfonol hydrazide. The SBO/FCS has been characterized by FT-IR, X-ray diffraction, XPS, HR-SEM, HR-TEM, AFM, and thermogravimetry (TGA).

Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI.

The growth of artificial intelligence (AI) in the healthcare industry tremendously increases the patient outcomes by reshaping the way we diagnose, treat and monitor patients. AI-based innovation in healthcare include exploration of drugs, personalized medicine, clinical diagnosis investigations, robotic-assisted surgery, verified prescriptions, pregnancy care for women, radiology, and reviewed patient information analytics. However, prediction of AI-based solutions are depends mainly on the implementation of statistical algorithms and input data set.

Artificial Neural Network Modeling of a CMOS Differential Low-Noise Amplifier Using the Bayesian Regularization Algorithm.

The purpose of this communication is to present the modeling of an Artificial Neural Network (ANN) for a differential Complementary Metal Oxide Semiconductor (CMOS) Low-Noise Amplifier (LNA) designed for wireless applications. For satellite transponder applications employing differential LNAs, various techniques, such as gain boosting, linearity improvement, and body bias, have been individually documented in the literature. The proposed LNA combines all three of these techniques differentially, aiming to achieve a high gain, a low noise figure, excellent linearity, and reduced power consumption.

AZ63/Ti/Zr Nanocomposite for Bone-Related Biomedical Applications.

Considering the unique properties of magnesium and its alloy, it has a vast demand in biomedical applications, particularly the implant material in tissue engineering due to its biodegradability. But the fixing spares must hold such implants till the end of the biodegradation of implant material. The composite technology will offer the added benefits of altering the material properties to match the requirements of the desired applications.

Optimal Cellular Microscopic Pattern Recognizer- (OCMPR-) Based Wireless Detection Network for Efficiently Leveraging the Parallel Distributed Processing Capabilities.

Recognizing patterns associated with particular events enables the detection of specific critical changes in the events. Due to the resource constraints inherent in WSNs, pattern recognition is highly dependent on the complexity of the computation, the number of iterations, and the requirements for node training. Iterative learning is frequently used in computer-based computer vision.

Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images.

Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis.

Efficient and Secure Privacy Analysis for Medical Big Data Using TDES and MKSVM with Access Control in Cloud.

Big Data and cloud computing are two essential issues in the recent years, empowers computing resources to be given as Information Technology services with high efficiency and effectiveness. So as to protect the security of data holders, data are regularly stored in the cloud in an encrypted form. In any case, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in the cloud along with access control.

Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy.

Diabetic Retinopathy (DR) has been a leading cause of blindness in case of human beings falling between the ages of 20 and 74 years. This will have a major influence on both the patient and the society as it can normally influence the humans in their gainful years. An early DR detection is quite challenging as it may not be detected by humans.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution.

Fabrication of magnetic particles imprinted cellulose based biocomposites for chromium(VI) removal.

The present study was focused on Cr(VI) removal using eco-friendly materials like cellulose (Cel), hydrotalcite (HT), hydroxyapatite (HAp) and their composite forms. The cellulose/hydrotalcite (CelHT) and cellulose/hydroxyapatite (CelHAp) composites were synthesized by dispersing HT and HAp individually in cellulose polymeric matrix. To enhance the Cr(VI) sorption capacity (SC) and easy separation, cellulose supported magnetic composites namely iron-oxide coated cellulose/hydrotalcite (FeO@CelHT) and cellulose/hydroxyapatite (FeO@CelHAp) were synthesized by in situ fabrication method.

Synthesis of assorted metal ions anchored alginate bentonite biocomposites for Cr(VI) sorption.

Biocomposites were synthesized by dispersing bentonite (Bent) clay in a biopolymer namely alginate (Alg) and cross-linked with bi (Ca(2+)), tri (Ce(3+)) and tetravalent (Zr(4+)) metal ions viz., Ca@AlgBent, Ce@AlgBent and Zr@AlgBent composites respectively. The synthesized biocomposites were characterized by various instrumental techniques like FTIR, SEM and EDAX.

One pot synthesis of metal ion anchored alginate-gelatin binary biocomposite for efficient Cr(VI) removal.

Biopolymers are widely used for the removal of chromium from aqueous medium but it possesses limitations like poor sorption capacity and low stability. To overcome the limitations of biopolymers and to improve their properties, the present study was designed in such a way to develop a novel sorbent with enhanced chromium sorption capacity and better stability by synthesizing metal ion cross-linked binary biocomposites using biopolymers like alginate and gelatin cross-linked with Ca2+, Ce3+ and Zr4+ ions namely Ca@AlgGel, Ce@AlgGel and Zr@AlgGel composites. The functional groups, agglomeration, surface area, surface morphology, elemental analysis and thermal stability of the composites were investigated by FTIR, TEM, BET, SEM with EDAX and TGA analysis.

A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image.

Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy.

Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients.

Synthesis of magnetic alginate hybrid beads for efficient chromium (VI) removal.

Recently magnetic bio-composites have attracted the attention of scientists because of their unique characteristics like selectivity and high sorption capacity. In the present study, Fe3O4@Alg-Ce magnetic composite beads were developed by incorporating Fe3O4 particles onto alginate (Alg) biopolymer followed by cross-linking with Ce(3+) ions. The synthesized magnetic beads were characterized using FTIR and SEM with EDAX analysis and utilized for chromium (VI) removal in batch mode.

Detection of dechallenge in spontaneous reporting systems: a comparison of Bayes methods.

Aim: Dechallenge is a response observed for the reduction or disappearance of adverse drug reactions (ADR) on withdrawal of a drug from a patient. Currently available algorithms to detect dechallenge have limitations. Hence, there is a need to compare available new methods.