Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in process Computer-Aided Diagnosis (CAD) systems. Segmenting considered a difficult task, particularly mammogram images because artefacts, homogeneity among muscle, low contrast along boundary, similarity between texture Region Interest (ROI), unwanted irregular ROI. This study aims to propose an improved threshold-based trainable segmentation model derive A hybrid approach for boundary was established...

One of the main causes increased mortality among women is breast cancer. The ultrasound scan most widely used method for diagnosing geological disease i.e. first step identifying abnormality cancer (malignant from benign), extraction region interest (ROI). In order to achieve this, a new approach ROI proposed purpose reducing false positive cases (FP). model was built based on local pixel information and neural network. It includes two stages namely, training testing. stage, trained by...

Breast cancer is one of the most prevalent types that plagues females. Mortality from breast could be reduced by diagnosing and identifying it at an early stage. To detect cancer, various imaging modalities can used, such as mammography. Computer-Aided Detection/Diagnosis (CAD) systems assist expert radiologist to diagnose This paper introduces findings a systematic review seeks examine state-of-the-art CAD for detection. based on 118 publications published in 2018–2021 retrieved major...

Breast cancer detection using mammogram images at an early stage is important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast from images. Hybrid thresholding and machine learning are used to derive region interest (ROI). The derived ROI then separated into five different blocks. wavelet transform applied suppress noise each produced block based on BayesShrink soft by capturing high low frequencies within sub-bands. An improved...

In developing countries breast cancer has been found to be one of the diseases that threatens lives women, and is why finding ways detecting efficiently great importance. The detection at an early stage through self-examination very difficult. this study, we proposed a new descriptor can help identify abnormality by enhancing features LBP texture enhance LPB using threshold important information for abnormal cases. next stage, significant are extracted from tumours images have segmented....

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite combination multiple to achieve superior ultrasound image by reducing speckle noise, an enhanced technique is not achieved. The purpose this study introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount above limitations aim study, new...

Nowadays, VANET (vehicular ad hoc network) is one of the key aspects developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, network exposes link failure that affects firmness causes delay congestion. Additionally, dynamic change in routing network's security, makes it vulnerable various attacks, data loss. In order overcome these drawbacks an efficient highly secured protocol needed. Subsequently, this research, a new proposed has...

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time data generation, and many other applications have been integrated into the ICPS platform. These encompass workflow tasks, such as processing within hospitals, laboratory tests, insurance companies patient payments, which necessitate a sequential flow. The external wireless, fog, cloud services face security issues that impact end-users'...

Abstract Malware software now encrypts the data of Internet Things (IoT) enabled fog nodes, preventing victim from accessing it unless they pay a ransom to attacker. The injunction is constantly accompanied by deadline. These days, ransomware attacks are too common on IoT healthcare devices. On other hand, IoT‐based heartbeat digital applications have been steadily increasing in popularity. make lot data, which send cloud be processed further. In networks, critical examine for malicious...

Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity images. While having accurate detection segmentation of would be beneficial, current methods still need solve this problem despite numerous available approaches. Precise analysis Magnetic Resonance Imaging (MRI) crucial for detecting, segmenting, classifying medical diagnostics. a vital component diagnosis, it requires precise, efficient, careful, reliable image techniques. The...

This paper presents the augmented Internet of Things (AIoT) framework for cooperatively distributed deep blockchain-assisted vehicle networks. AIoT splits application into various tasks while executing them on different computing nodes. The has constraints, such as security, time, and accuracy, which are considered during processing parallel nodes (e.g., fog cloud). We propose a partitioned scheme, dividing vehicular local remote tasks. objective is to minimize delays efficiently execute...

Abstract Coronavirus disease 2019 (COVID‐19) has attracted significant attention of researchers from various disciplines since the end 2019. Although global epidemic situation is stabilizing due to vaccination, new COVID‐19 cases are constantly being discovered around world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, been used ameliorate diagnosis. It helps reveal missed diagnoses ambiguity nucleic acid polymerase chain reaction....

Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them surface defects. Analysis EL has been manually performed by visual inspection experts. This manual procedure tedious, time-consuming, subjective, requires deep expert knowledge. In this work, hybrid fully-automated classification system developed detecting different types defects in images. The fuses the feature representations extracted from two learning models (Inception-V3...

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort brain tumour a patient but also helps them diagnose and cure these tumours. This has been proven more useful those developing countries where available health-based funding-based resources are limited. By employing set-based operations arithmetical model, namely fuzzy parameterised complex intuitionistic hypersoft set (FPCIFHSS), this study seeks to...

Breast cancer (BC) is a main killer disease for women and men. It can be cured controlled only if it detected at its early detection. BC initial identification realized by the help of computer support approaches. From detailed study on previous researches, found that, there no system producing high accuracy because one or more reasons. Absence effective preprocessing discussed reason that obstructs detection Computer-aided diagnosis (CAD) method. Noise removal contrast enhancement are two...

<span>This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce robust method. Furthermore, increasing security encryption of the watermark is done prior embedding operation. In proposed scheme, actual image altered four sub-band over three levels then embedded LH transformed original image. operation, multiple scaling factors...

Deep learning models possess the ability to precisely analyze medical images such as MRI, CT scans, and ultrasound images. This automated diagnostic process facilitates early detection of kidney disease by identifying any abnormalities or signs disease. Consequently, it allows for timely intervention treatment, while also reducing need manual interpretation radiologists clinicians. As a result, diagnosis is expedited, leading improved efficiency in healthcare. The proposed technique focuses...

Various diseases have recently wreaked influence on people's way of life. Several bone illnesses significantly the quality life, including Knee Osteoarthritis. When cartilage in knee joint between femur and tibia wears down, it causes Osteoarthritis, which results significant j oint pain, movement restrictions, gait abnormalities, even effusion. This study presents a method based deep features. We employed Convolutional Neural Network to extract features from Osteoarthritis images. Then,...

In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in ticketing, allowing validation with more data source different transport modes. This paper proposes novel technology-based mobile application-based system. We formulate the problem as multi-agent reinforcement learning framework ticketing multi-transport environments. Specifically, we propose Asynchronous Advantage Critic Biometric...