Data are being generated and used to support all aspects of healthcare provision, from policy formation the delivery primary care services. Particularly, with change emphasis curative preventive medicine, importance data-based research such as data mining machine learning has emphasized issues class distributions in datasets. In typical predictive modeling, inability effectively address a imbalance real-life dataset is an important shortcoming existing algorithms. Most algorithms assume...

The increasing prevalence of IoT devices has brought about numerous security challenges due to their relatively simple internal architecture and low-powered hardware warranted by small footprint requirement. As there are billions in use today, the sheer number such pose a great challenge as they often constrained software limitations addition being designed with focus on convenience, ease use, mass production, low cost, rather than security. seemingly exponentially make it harder keep track...

The dependence on smart city applications has expanded in recent years. Consequently, the number of cyberattack attempts to exploit application vulnerabilities significantly increases. Therefore, improving security during software development process is mandatory ensure sustainable cities. But challenge how adopt practices process. There are Several established and mature testing frameworks exist that consider requirements already Software Development Life Cycle (SDLC), but there a unique...

Abstract The federated learning has gained prominent attention as a collaborative machine method, allowing multiple users to jointly train shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed by introducing an innovative hybrid methodology fusing differential with (HDP‐FL). Through meticulous experimentation on EMNIST CIFAR‐10 sets, this approach yields substantial advancements, showcasing...

Abstract Interactions between individuals and digital material have completely changed with the advent of Metaverse. Due to this, there is an immediate need construct cutting‐edge technology that can recognize emotions users continuously provide relevant their psychological states, improving overall experience. An inventive method combines natural language processing adaptive content generation algorithms neuro‐fuzzy‐based support vector machines (SVM‐NLP) proposed by researchers meet this...

A stroke is a medical condition characterized by the rupture of blood vessels within brain which can lead to damage. various symptoms may be exhibited when brain's supply and essential nutrients disrupted. To forecast possibility occurring at an early stage using Machine Learning Deep main objective this study. Timely detection warning signs significantly reduce its severity. This paper performed comprehensive analysis features enhance prediction effectiveness. reliable dataset for taken...

Regular commutes to work can cause chronic stress, which in turn a physical and emotional reaction. The recognition of mental stress its earliest stages is very necessary for effective clinical treatment. This study investigated the impact commuting on human health based qualitative quantitative measures. measures included electroencephalography (EEG) blood pressure (BP), as well weather temperature, while were established from PANAS questionnaire, age, height, medication, alcohol status,...

Artificial intelligenceAI advancement in the financial sector to support investment management, digital advisers can making better decisions by providing recommendations for diversified portfolios using machine learning algorithms. There is an increasing demand advisors knockout innovations industry, where a portfolio managed through artificial intelligence and This research seeks establish effect of investor competency literacy expertise on advisors. An analysis ten core articles, from 2019...

Current treatments for bipolar depression have limited effectiveness, tolerability and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain method that has demonstrated treatment efficacy major depressive episodes. tDCS portable, safe, individuals like having sessions at home. We developed home-based protocol with real-time remote supervision. In the present study, we examined clinical outcomes, acceptability feasibility of in depression.

Tick-borne diseases are a significant health risk to humans and animals worldwide. It is important understand the environmental climatic factors that contribute tick occurrence rates in order reduce proliferation of tick-borne diseases. Using machine learning spatial indexing techniques, this study covers Europe over last 20 years Ixodes ricinus abundance. We used biodiversity databases land cover categories, climate, vegetation index, sociological factors. Areas with agriculture natural...

Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment response to treatment, radiotherapy planning. Many techniques have been proposed segmenting medical data; however, some the approaches poor performance, large inaccuracy, require substantial computation time analysing volumes. Artificial intelligence (AI) can provide improved accuracy save decent amount time....

Curvelet transform is a new extension of wavelet which aims to deal with interesting phenomena occurring along curves. particularly challenging task classify human organs in CT scans using gray-level information. An efficient implementation curvelet for medical image segmentation and denoising has been presented this paper. A comparison study carried out paper between different transforms reveals that exhibits optimal representation the region interest (ROI) better accuracy less noise.

3D volume segmentation aims at partitioning the voxels into objects (sub-volumes) which represent meaningful physical entities. Multi-resolution analysis (MRA) allows for preservation of an image according to certain levels resolution or blurring. The quality this approach makes it useful in compression, de-noising, and classification segmentation. This paper focuses on implementation a medical technique using different wavelet decompositions such as discrete transform (DWT) packet (WP). A...

Abstract Current treatments for bipolar depression have limited effectiveness, tolerability and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain method that has demonstrated treatment efficacy major depressive episodes. tDCS portable, safe, individuals like having sessions at home. We developed home-based protocol with real-time remote supervision. In the present study, we examined clinical outcomes, acceptability feasibility of in depression....

This review paper explores the application of Artificial Intelligence (AI) in concrete mix design and its impact on industry. The traditional approaches to are first discussed, highlighting their limitations. Subsequently, various applications AI presented, including optimal proportioning mixes, prediction properties, quality control assurance, strength optimisation durability assessment enhancement. benefits industry then examined, emphasising advantages using design. However, challenges...

The predominant application of positron emission tomography (PET) in the field oncology and radiotherapy significance medical imaging research have led to an urgent need for effective approaches PET volume analysis development accurate robust techniques support oncologists their clinical practice, including diagnosis, arrangement appropriate treatment, evaluation patients' response therapy. This paper proposes efficient optimized ensemble classifier tackle problem squamous cell carcinoma...