One of the most frequent cancers in women is breast cancer, and year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 died from this cancer. An early diagnosis cancer can help to overcome mortality rate. However, manual using mammogram images not an easy process always requires expert person. Several AI-based techniques suggested literature. still, they are facing several challenges, such as similarities between non-cancer regions, irrelevant feature extraction,...

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm research. This boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite considerable volume existing research, greater portion it has primarily concentrated on activities. Comparatively less emphasis been placed recognition localization patterns. In current study, we...

Abstract A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of most deep architecture classification (ii) an expert field who can assess output models. These difficulties motivate us to propose efficient and accurate system based on evolutionary optimization four...

One of the main problems with WSNs is that most sensor nodes in wireless networks (WSNs) are motorized by energy-constrained, which significantly affects system's effectiveness, dependability, and lifespan. Numerous clustering strategies have been created to enhance energy efficiency 5G 6G transmission. To overcome these issues, we suggest a collaborative energy-efficient routing protocol (CEEPR) for sustainable communication 5G/6G (WSNs). Initially, this study gathered collected data at...

Gesture recognition in dynamic images is challenging computer vision, automation and medical field. Hand gesture tracking between both human must have symmetry real world. With advances sensor technology, numerous researchers recently proposed RGB techniques. In our research paper, we introduce a reliable hand model that accurate despite any complex environment, it can track recognise gestures. Firstly, videos are converted into frames. After light intensity adjustment noise removal, passed...

Abstract Diseases of the Gastrointestinal (GI) tract significantly affect quality human life and have a high fatality rate. Accurate diagnosis GI diseases plays pivotal role in healthcare systems. However, processing large amounts medical image data can be challenging for radiologists other professionals, increasing risk inaccurate assessments. Computer‐aided Diagnosis systems provide help to doctors rapid accurate diagnosis, thus resulting saving lives. Recently, many techniques are found...

Abstract Currently, the improvement in AI is mainly related to deep learning techniques that are employed for classification, identification, and quantification of patterns clinical images. The models show more remarkable performance than traditional methods medical image processing tasks, such as skin cancer, colorectal brain tumour, cardiac disease, Breast cancer (BrC), a few more. manual diagnosis issues always requires an expert also expensive. Therefore, developing some computer based...

Autonomous vehicle detection and tracking are crucial for intelligent transportation management control systems. Although many techniques used to develop smart traffic systems, this article discusses using pixel-labeling real-time tracking. We propose a novel system that segments the image an Extreme Gradient Boost (XGBoost) classifier extract foreground objects. The proposed model is divided into following steps: 1) at first, all images preprocessed remove noise; 2) performed by XGBoost...

Ubiquitous computing has been a green research area that managed to attract and sustain the attention of researchers for some time now. As ubiquitous applications, human activity recognition localization have also popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, entertainment. A robust model proposed this article works over IoT data extracted from smartphone smartwatch sensors recognize activities performed by user and, meantime,...

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends changing from physical infrastructure to online services. the restrictions imposed during COVID-19, medical services have been changed. The concepts of homes, appliances, gained Internet Things (IoT) has revolutionized communication data collection by incorporating sensors for diverse sources. In addition, it utilizes artificial intelligence (AI) approaches...

Abstract Billions of gadgets are already online, making the IoT an essential aspect daily life. However, interconnected nature devices also leaves them open to cyber threats. The quantity and sophistication assaults aimed against Internet Things (IoT) systems have skyrocketed in recent years. This paper proposes a next-generation attack prediction framework for systems. uses multi-class support vector machine (SVM) improved CHAID decision tree learning methods. traffic is classified using...

Abstract Abnormalities in the heart's rhythm, known as arrhythmias, pose a significant threat to global health, often leading severe cardiac conditions and sudden deaths. Therefore, early accurate detection of arrhythmias is crucial for timely intervention potentially life‐saving treatment. Artificial Intelligence, particularly deep learning, has revolutionised diagnosis various health conditions, including arrhythmias. A unique hybrid architecture, ECG‐TransCovNet, that combines...

The demand for a non-contact biometric approach candidate identification has grown over the past ten years. Based on most important application, human gait analysis is significant research topic in computer vision. Researchers have paid lot of attention to recognition, specifically people based their walking patterns, due its potential correctly identify far away. Gait recognition systems been used variety applications, including security, medical examinations, identity management, and...

Parkinson disease affect bodily functions and there is a growing need for advanced solutions to offer therapeutic advice patients. A framework using arti- facial intelligence machine learning techniques has been proposed address this. The system employs combination of RGB, inertial, depth sensors data. inertial signals have filtered notch filter obtain the optimal wearable sensor data by examining upper lower cutoff frequencies. Multiple features calculated, including mel frequency cepstral...

Machines need to be able recognize and understand complex visual surroundings function at their best in a variety of contexts. Here, we address the difficult problem multi-object recognition obtain sophisticated knowledge environments, tackling issues such as size, occlusion, fluctuations object traits, complicated backdrops. Our contribution is provide novel methods (Gaussian mixture model mean-shift algorithms) for inferring multiple segmentation visuals, introducing unique multiclass...

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role DR detection at initial phase cure vision loss. Therefore, method is proposed this study that consists of three models for localization, segmentation, and classification. A novel technique designed with combination pre-trained ResNet-18 YOLOv8 based on selection optimum layers localization lesions. The localized images are passed semantic segmentation...

Systems must be capable of detecting and tracking autonomous vehicles for intelligent management control transportation. Even though several methods are used to create systems traffic monitoring, this article explains how detect track using pixel labeling particle filter algorithms. We suggested a novel technique that segments the image segmentation retrieve foreground objects. have divided our proposed model into following steps: at first, geo-referencing is find exact location; secondly,...

The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing proper treatment. Deep learning techniques are commonly used feature extraction have demonstrated significant performance in the literature. However, these features do not perform well several cases due to redundant irrelevant information. We created a new framework diagnosing entropy-controlled deep flower pollination optimization from images. In proposed framework,...

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in eyes, with ground occupied lesions varied extent determining disease burden. This is among most common cause visual impairment working population. Various factors have been discovered to play an important role a person's growth this condition. Among essential elements at top list anxiety long-term diabetes. If not detected early, might result permanent...

Refactoring tools have advanced greatly and are being used in many large projects. As a result, great deal of information is now available about past refactoring its effects on the source code. However, when multiple performed at once, it becomes more difficult to analyze their impact. visualization can help developers create maintainable code that easier understand modify over time. Although there an increasing interest visualizing changes software engineering research, has been relatively...

Object segmentation and recognition is an imperative area of computer vision machine learning that identifies separates individual objects within image or video determines classes categories based on their features.The proposed system presents a distinctive approach to object using Artificial Neural Networks (ANNs).The takes RGB images as input uses k-means clustering-based technique fragment the intended parts into different regions label them characteristics.Then, two distinct kinds...