The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing non-linear nature of size, shape, textural variation. Radiologists, clinical experts, surgeons examine MRI scans available methods, which are tedious, error-prone, time-consuming, still exhibit positional accuracy up 2–3 mm, very high case cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection...

Accurate radiogenomic classification of brain tumors is important to improve the standard diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused radiomic predicting genetic subtype A fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, proposed feature extraction fuses quantitative...

Cervical cancer (CC), the most common among women, is commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes promising tool improving CC diagnosis. Deep learning (DL), branch of AI, holds particular potential in CAD systems early detection and accurate DL algorithm trained to identify abnormalities patterns smear images, such as dysplasia,...

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing its advantages, such as low cost, simplicity, availability. In order reduce severity of epileptic seizures, it necessary design techniques identify disease at earlier stage. Since traditional way diagnosing laborious time-consuming, automated tools...

In the present era, cancer is leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods improve prediction performance. medical image processing, enhancement can further This study aimed lung quality by utilizing employing various methods, such as adjustment, gamma correction, contrast stretching, thresholding, histogram equalization methods. We extracted gray-level co-occurrence...

Recently, Telehealth connects patients to vital healthcare services via remote monitoring, wireless communications, videoconferencing, and electronic consults. By increasing access specialists physicians, telehealth assists in ensuring receive the proper care at right time place. Teleophthalmology is a study of telemedicine that provides for eye using digital medical equipment telecommunication technologies. Multimedia computing with Explainable Artificial Intelligence (XAI) has potential...

Remote sensing image (RSI) scene classification has become a hot research topic due to its applicability in different domains such as object recognition, land use classification, retrieval, and surveillance. During RSI process, class label will be allocated every based on the semantic details, which is significant real-time applications mineral exploration, forestry, vegetation, weather, oceanography. Deep learning (DL) approaches, particularly convolutional neural network (CNN), have shown...

The recent combination of ambient backscatter communication (ABC) with non-orthogonal multiple access (NOMA) has shown great potential for connecting large-scale Internet Things (IoT) in future unmanned aerial vehicle (UAV) networks. basic idea ABC is to provide battery-free transmission by harvesting the energy existing RF signals WiFi, TV towers, and cellular base stations/UAV. uses smart sensor tags modulate reflect data among wireless devices. On other side, NOMA makes possible more than...

The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet Things (IoT) needs continuously accessible end-to-end routes. However, node (SN) relies on a limited power source tends to cause disconnection multi-hop routes because shortage WSN, eventually leading inefficiency total IoT network. Furthermore, density available SNs affects existence feasible level path multiplicity WSN. Thus, an effective routing model is predictable extend lifetime...

Particulate matter is emitted from diverse sources and affect the human health very badly. Dust particles exposure stated environment can our heart lungs The particle pollution creates a variety of problems including nonfatal attacks, premature deaths in people with lung or disease, asthma, difficulty breathing, etc. In this article, we developed an automated tool by computing multimodal features to capture dynamics ambient particulate then applied Chi-square feature selection method acquire...

Increasing waste generation has become a key challenge around the world due to dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via Internet of things (IoT). It is limited controlling bad odor blowout gases spreading overspills by using an IoT-based solution. The inadequate poor dumping produces radiation toxic environment, creating adversarial effect global warming, human health,...

Barnacles Mating Optimizer (BMO) is a new metaheuristic algorithm that suffers from slow convergence and poor efficiency due to its limited capability in exploiting the search space exploring promising regions. Addressing these shortcomings, this paper introduces Elitist (eBMO). Unlike BMO, eBMO exploits elite exponential probability (Pelite) decide whether intensify process via swap operator or diversify by randomly Furthermore, uses Chebyshev map instead of random numbers generate quality...

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances vision and artificial intelligence (AI) techniques have resulted the design of CAD models, which enables to detection existence diseases using various imaging modalities. Oral cancer (OC) has commonly occurred head neck globally. Earlier identification OC improve survival rate reduce mortality rate. Therefore, classification essential. this study introduces a novel...

Sign language includes the motion of arms and hands to communicate with people hearing disabilities. Several models have been available in literature for sign detection classification enhanced outcomes. But latest advancements computer vision enable us perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Language Gesture Classification Deer Hunting Optimization Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly...

Oral cancer is the most commonly occurring 'head and neck cancers' across globe. Most of oral cases are diagnosed at later stages due to absence awareness among public. Since earlier identification disease essential for improved outcomes, Artificial Intelligence (AI) Machine Learning (ML) models used in this regard. In background, current study introduces with Deep Transfer driven Cancer detection Classification Model (AIDTL-OCCM). The primary goal proposed AIDTL-OCCM model diagnose using AI...

Recently, Internet of Things (IoT) has been developed into a field research and it purposes at linking many sensors enabling devices mostly to data collection track applications. Wireless sensor network (WSN) is vital element IoT paradigm since its inception one the chosen platforms for deploying smart city application regions such as disaster management, intelligent transportation, home automation, buildings, other IoT-based application. The routing approaches were extremely-utilized energy...

Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, study designs chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for...

The adoptability of the heart to external and internal stimuli is reflected by rate variability (HRV). Reduced HRV can be a predictor post-infarction mortality. In this study, we propose an automated system predict diagnose congestive failure using short-term analysis. Based on nonlinear, nonstationary, highly complex dynamics failure, extracted multimodal features capture temporal, spectral, dynamics. Recently, Bayesian inference approach has been recognized as attractive option for deeper...

Currently, software development is more associated with families of configurable rather than the single implementation a product. Due to numerous possible combinations in product line, testing these lines (SPLs) difficult undertaking (SPL). Moreover, presence optional features makes SPLs impractical. Several are presented SPLs, but due environment's time and financial constraints, rendered unfeasible. Testing subsets configured products thus one approach solving this issue. In order...

The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate precise to plan our activities safeguard ourselves well property from disasters. Rainfall, wind speed, humidity, direction, cloud, temperature, other variables used this work for prediction. Many research works have been conducted on forecasting. drawbacks of existing...

The gunshot event localization and classification have numerous real-time applications. study is also useful for steering the video camera guns in directed direction. This paper proposes a framework that can be used surveillance system to accurately localize classify type of gunshots impregnated with wind noise. main contribution this very first time using Hadamard product wavelet de-noising windy conditions. We evaluated our on airborne acoustic dataset, derived (simulated) sound as an...

Breast cancer is the second most dominant kind of among women. Ultrasound images (BUI) are commonly employed for detection and classification abnormalities that exist in breast. The ultrasound necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving performance, Computer Aided Diagnosis (CAD) models useful breast classification. current advancement deep learning (DL) model enables with use biomedical images. With this motivation, article...

The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving diagnosing rate cancer. results existing lesion techniques are not up to mark for with artifacts like varying size corner borders color similar and/or hairs having low contrast surrounding background. To improve such kinds images, an method is proposed this research work. searches presence given dermoscopc image and removes them if found otherwise it starts...

Computational linguistics is the scientific and engineering discipline related to comprehending written spoken language from a computational perspective building artefacts that effectively process produce language, either in bulk or dialogue setting.This paper develops Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification dictarization (CBSOEDL-APCD) technique.The presented CBSOEDL-APCD technique involves of text into poetries prose.Primarily, carries...