Abstract This paper explores the concept of smart cities and role Internet Things (IoT) machine learning (ML) in realizing a data-centric environment. Smart leverage technology data to improve quality life for citizens enhance efficiency urban services. IoT have emerged as key technologies enabling city solutions that rely on large-scale collection, analysis, decision-making. presents an overview cities’ various applications discusses challenges associated with implementing environments. The...

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection video Speech Recognition. CNN is a special type of Neural Network, which compelling effective learning ability to learn features at several steps during augmentation the data. Recently, interesting inspiring...

Around the world, brain tumors are becoming leading cause of mortality. The inability to undertake a timely tumor diagnosis is primary this pandemic. Brain cancer crucial procedure that relies on expertise and experience doctor. Radiologists must use an automated classification model find cancers. current model's accuracy has be improved get suitable therapies. can consult various computer-aided diagnostic (CAD) models in literature medical imaging assist them with their patients. Previous...

Patients with breast cancer are prone to serious health-related complications higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due technical issues imaging qualities and heterogeneous densities which increases the false-(positive negative) ratio. Early intervention is significant establishing an up-to-date prognosis process can successfully mitigate disease recovery. manual screening abnormalities through traditional machine...

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. detection needs accurate mammography interpretation and analysis, which challenging for radiologists owing to intricate anatomy breast low image quality. Advances in deep learning-based models have significantly improved lesions' detection, localization, risk assessment, categorization. This study proposes novel convolutional neural network (ConvNet) that reduces human error diagnosing malignancy...

The advent of healthcare information management systems (HIMSs) continues to produce large volumes data for patient care and compliance regulatory requirements at a global scale. Analysis this big allows boundless potential outcomes discovering knowledge. Big analytics (BDA) in can, instance, help determine causes diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency the delivery effectiveness viability treatments, accurate predictions readmissions, clinical...

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis decision-making, aiding intelligent services better disease management recovery. Due to unique nature images, algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, negatives. In view these problems, researchers primarily improve network structure but rarely from unstructured aspect. The paper tackles...

A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) robust hybrid particle swarm optimization (HPSO) approach. PSO coupled with spiral-shaped mechanism (HPSO-SSM) to optimize the performance by mitigating its constraints, such as sluggish convergence local minimum dilemma. Three advancements are incorporated into hypothesized HPSO-SSM algorithms achieve remarkable results. First, diversity of search process promoted...

In this era, there are plenty of wireless devices that being used with the support WI-FI (Wireless Fidelity) and they need to be maintained authorized. Wireless Sensor Networks (WSN), a cornerstone modern technology, offer cost-efficient solutions for diverse monitoring tasks but simultaneously exposed myriad security threats, including unauthorized access, attacks, suspicious activities. These vulnerabilities can significantly degrade performance reliability WSNs, making early detection...

This paper focuses on facilitating state-of-the-art applications of big data analytics ( BDA ) architectures and infrastructures to telecommunications telecom industrial sector. Telecom companies are dealing with terabytes petabytes a daily basis. IoT in further contributing this deluge. Recent advances have exposed new opportunities get actionable insights from data. These benefits the fast-changing technology landscape make it important investigate existing For this, we initially determine...

Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids mitigating the disease's complexities curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of false-positive findings, reduces patient's survival chances. Consequently, approaches that learn discern can reduce number misconceptions incorrect diagnoses. Conventionally used classification models focus on feature extraction...

Microcalcification clusters in mammograms are one of the major signs breast cancer. However, detection microcalcifications from is a challenging task for radiologists due to their tiny size and scattered location inside denser composition. Automatic CAD systems need predict cancer at early stages support clinical work. The intercluster gap, noise between individual MCs, object’s can affect classification performance, which may reduce true-positive rate. In this study, we propose...

Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched the first time, as in situation viral epidemic, handling it with limited might be difficult. Additionally, quite unbalanced this situation, findings coming from significant instances novel illness. We offer technique that allows class balancing algorithm to understand and detect lung disease signs chest X-ray CT images. Deep learning techniques used train evaluate images, enabling...

Ischemic Cardiovascular diseases are one of the deadliest in world. However, mortality rate can be significantly reduced if we detect disease precisely and effectively. Machine Learning (ML) models offer substantial assistance to individuals requiring early treatment detection realm cardiovascular health. In response this critical need, study developed a robust system predict ischemic accurately using ML-based algorithms. The dataset obtained from Kaggle encompasses comprehensive collection...

Abstract Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time detection using fundus cameras address this. This research aims develop efficient timely assistance patients, empowering them manage their health better. The proposed leverages imaging...