An optimal deep learning approach for breast cancer detection and classification with pre-trained CNN-based feature learning mechanism.

Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as the second highest cause of death for women worldwide. Early BC prediction helps decrease the BC mortality rate and improve treatment plans. Ultrasound is a popular and widely used imaging technique to detect BC at an earlier stage.

Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading With Rat Swarm Optimization Algorithm for Enhanced Capsule Generation Adversarial Network.

In the worldwide working-age population, visual disability and blindness are common conditions caused by diabetic retinopathy (DR) and diabetic macular edema (DME). Nowadays, due to diabetes, many people are affected by eye-related issues. Among these, DR and DME are the two foremost eye diseases, the severity of which may lead to some eye-related problems and blindness.

Brain tumor classification for MRI images using dual-discriminator conditional generative adversarial network.

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using Dual-Discriminator Conditional Generative Adversarial Network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality.

Automatic nutrient estimator: distributing nutrient solution in hydroponic plants based on plant growth.

Background: The primary objective is to address the specific needs of plants at different growth stages by delivering precise nutrient concentrations tailored to their developmental requirements. Challenges such as uneven nutrient distribution, fluctuations in pH and electrical conductivity, and inadequate nutrient delivery pose potential hindrances to achieving optimal plant health and yield in hydroponic systems. By overcoming these challenges, the hydroponic farming community aims to enhance the accuracy of nutrient dosing, streamline automation processes, and minimize resource wastage.

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.

Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field.

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region.

Aim: Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. .

Synthesis, Structural, Spectroscopic, Fluorescence, and Dielectric Studies of Bis-(4-Aminopyridine)-Zinc(II) Acetate: A Metal-Organic Crystal.

The advancement of crystalline growth and characterization tools allows us to investigate novel nonlinear optical substances suitable for photonic applications. Bis-(4-aminopyridine)-zinc(II) acetate (B4AZA), a metal-organic crystal was produced in this study using the slow evaporation procedure at room temperature. Analytical studies such as X-ray crystallography, Fourier transform infrared (FT-IR), UV-visible (UV-Vis), fluorescence, second harmonic generation (SHG), and dielectric tests were used to characterize the as-grown B4AZA crystals.

Deep convolutional neural network based hyperspectral brain tissue classification.

Background: Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspectral images (HSI) is challenging due to the minimum availability of training samples.

Objective: To overcome this challenge, this study proposes applying a 3D-CNN (convolution neural network) model to process spatial and temporal features and thus improve performance of tumor image classification.

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus.

Emerging Trends in Porogens toward Material Fabrication: Recent Progresses and Challenges.

Fabrication of tailor-made materials requires meticulous planning, use of technical equipments, major components and suitable additives that influence the end application. Most of the processes of separation/transport/adsorption have environmental applications that demands a material to be with measurable porous nature, stability (mechanical, thermal) and morphology. Researchers say that a vital role is played by porogens in this regard.

An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction.

Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases.

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN.

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain's function. EEG is an effective instrument for deciphering the brain's complicated activity.

Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer.

Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information.

Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification.

In this article, COVID-19 detection and classification framework based on anopheles search optimized AlexNet convolutional deep neural network for random forest classifier is implemented. Here, the COVID-19 dataset is taken from Joseph Paul Cohen database. Then, the input images are preprocessed with the help of fuzzy gray level difference histogram equalization technique (FGLHE) and fuzzy stacking technique for color enhancement and noise elimination in the input images.

An ensemble approach for healthcare application and diagnosis using natural language processing.

Integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing.

Fuzzy segmentation and black widow-based optimal SVM for skin disease classification.

The skin, which has seven layers, is the main human organ and external barrier. According to the World Health Organization (WHO), skin cancer is the fourth leading cause of non-fatal disease risk. In medicinal fields, skin disease classification is a major challenging issue due to inaccurate outputs, overfitting, larger computational cost, and so on.

Real-time Dash Streaming Architecture for Internet of Things Using FBMRWP Model for Medical Videos.

Background: The proposed method uses random adjustments in the online video quality based on the bandwidth allocated over Dynamic Adaptive Streaming over HTTP (DASH) streaming service.

Aim: The main objective is to improve the video quality from DASH-HTTP servers with variable bandwidth. Here, the system is adjusted dynamically for providing best video quality services based on the requirement of the user.

Universal Keyword Classifier on Public Key Based Encrypted Multikeyword Fuzzy Search in Public Cloud.

Cloud computing has pioneered the emerging world by manifesting itself as a service through internet and facilitates third party infrastructure and applications. While customers have no visibility on how their data is stored on service provider's premises, it offers greater benefits in lowering infrastructure costs and delivering more flexibility and simplicity in managing private data. The opportunity to use cloud services on pay-per-use basis provides comfort for private data owners in managing costs and data.

Direction based Hazard Routing Protocol (DHRP) for disseminating road hazard information using road side infrastructures in VANETs.

This paper presents Direction based Hazard Routing Protocol (DHRP) for disseminating information about fixed road hazards such as road blocks, tree fall, boulders on road, snow pile up, landslide, road maintenance work and other obstacles to the vehicles approaching the hazardous location. The proposed work focuses on dissemination of hazard messages on highways with sparse traffic. The vehicle coming across the hazard would report the presence of the hazard.